Overview

Dataset statistics

Number of variables22
Number of observations2938
Missing cells2563
Missing cells (%)4.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory505.1 KiB
Average record size in memory176.0 B

Variable types

Text1
Numeric20
Categorical1

Alerts

life_expectancy is highly overall correlated with adult_mortality and 12 other fieldsHigh correlation
adult_mortality is highly overall correlated with life_expectancy and 2 other fieldsHigh correlation
infant_deaths is highly overall correlated with life_expectancy and 5 other fieldsHigh correlation
alcohol is highly overall correlated with income_composition_of_resources and 2 other fieldsHigh correlation
percentage_expenditure is highly overall correlated with gdp and 1 other fieldsHigh correlation
hepatitis_b is highly overall correlated with polio and 1 other fieldsHigh correlation
measles is highly overall correlated with infant_deaths and 1 other fieldsHigh correlation
bmi is highly overall correlated with life_expectancy and 5 other fieldsHigh correlation
under_five_deaths is highly overall correlated with life_expectancy and 6 other fieldsHigh correlation
polio is highly overall correlated with life_expectancy and 4 other fieldsHigh correlation
diphtheria is highly overall correlated with life_expectancy and 4 other fieldsHigh correlation
hiv_aids is highly overall correlated with life_expectancy and 5 other fieldsHigh correlation
gdp is highly overall correlated with life_expectancy and 5 other fieldsHigh correlation
thinness__1_19_years is highly overall correlated with life_expectancy and 4 other fieldsHigh correlation
thinness_5_9_years is highly overall correlated with life_expectancy and 4 other fieldsHigh correlation
income_composition_of_resources is highly overall correlated with life_expectancy and 14 other fieldsHigh correlation
schooling is highly overall correlated with life_expectancy and 12 other fieldsHigh correlation
status is highly overall correlated with life_expectancy and 3 other fieldsHigh correlation
alcohol has 194 (6.6%) missing valuesMissing
hepatitis_b has 553 (18.8%) missing valuesMissing
bmi has 34 (1.2%) missing valuesMissing
total_expenditure has 226 (7.7%) missing valuesMissing
gdp has 448 (15.2%) missing valuesMissing
population has 652 (22.2%) missing valuesMissing
thinness__1_19_years has 34 (1.2%) missing valuesMissing
thinness_5_9_years has 34 (1.2%) missing valuesMissing
income_composition_of_resources has 167 (5.7%) missing valuesMissing
schooling has 163 (5.5%) missing valuesMissing
infant_deaths has 848 (28.9%) zerosZeros
percentage_expenditure has 611 (20.8%) zerosZeros
measles has 983 (33.5%) zerosZeros
under_five_deaths has 785 (26.7%) zerosZeros
income_composition_of_resources has 130 (4.4%) zerosZeros

Reproduction

Analysis started2023-05-25 00:55:14.357166
Analysis finished2023-05-25 00:55:46.770110
Duration32.41 seconds
Software versionydata-profiling vv4.2.0
Download configurationconfig.json

Variables

Distinct193
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Memory size23.1 KiB
2023-05-24T18:55:46.914134image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Length

Max length52
Median length34
Mean length10.041184
Min length4

Characters and Unicode

Total characters29501
Distinct characters56
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)0.3%

Sample

1st rowAfghanistan
2nd rowAfghanistan
3rd rowAfghanistan
4th rowAfghanistan
5th rowAfghanistan
ValueCountFrequency (%)
republic 192
 
4.5%
of 192
 
4.5%
and 97
 
2.3%
united 64
 
1.5%
democratic 48
 
1.1%
the 48
 
1.1%
guinea 48
 
1.1%
saint 33
 
0.8%
ireland 32
 
0.7%
congo 32
 
0.7%
Other values (223) 3502
81.7%
2023-05-24T18:55:47.189912image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 4190
 
14.2%
i 2535
 
8.6%
e 2178
 
7.4%
n 2104
 
7.1%
o 1638
 
5.6%
r 1635
 
5.5%
1350
 
4.6%
u 1126
 
3.8%
l 1110
 
3.8%
t 1107
 
3.8%
Other values (46) 10528
35.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 23976
81.3%
Uppercase Letter 3967
 
13.4%
Space Separator 1350
 
4.6%
Open Punctuation 64
 
0.2%
Close Punctuation 64
 
0.2%
Other Punctuation 48
 
0.2%
Dash Punctuation 32
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 4190
17.5%
i 2535
10.6%
e 2178
 
9.1%
n 2104
 
8.8%
o 1638
 
6.8%
r 1635
 
6.8%
u 1126
 
4.7%
l 1110
 
4.6%
t 1107
 
4.6%
d 867
 
3.6%
Other values (17) 5486
22.9%
Uppercase Letter
ValueCountFrequency (%)
S 466
 
11.7%
B 336
 
8.5%
C 289
 
7.3%
M 275
 
6.9%
A 256
 
6.5%
G 240
 
6.0%
R 240
 
6.0%
T 209
 
5.3%
I 194
 
4.9%
P 193
 
4.9%
Other values (14) 1269
32.0%
Space Separator
ValueCountFrequency (%)
1350
100.0%
Open Punctuation
ValueCountFrequency (%)
( 64
100.0%
Close Punctuation
ValueCountFrequency (%)
) 64
100.0%
Other Punctuation
ValueCountFrequency (%)
' 48
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 27943
94.7%
Common 1558
 
5.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 4190
15.0%
i 2535
 
9.1%
e 2178
 
7.8%
n 2104
 
7.5%
o 1638
 
5.9%
r 1635
 
5.9%
u 1126
 
4.0%
l 1110
 
4.0%
t 1107
 
4.0%
d 867
 
3.1%
Other values (41) 9453
33.8%
Common
ValueCountFrequency (%)
1350
86.6%
( 64
 
4.1%
) 64
 
4.1%
' 48
 
3.1%
- 32
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29485
99.9%
None 16
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 4190
 
14.2%
i 2535
 
8.6%
e 2178
 
7.4%
n 2104
 
7.1%
o 1638
 
5.6%
r 1635
 
5.5%
1350
 
4.6%
u 1126
 
3.8%
l 1110
 
3.8%
t 1107
 
3.8%
Other values (45) 10512
35.7%
None
ValueCountFrequency (%)
ô 16
100.0%

year
Real number (ℝ)

Distinct16
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2007.5187
Minimum2000
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-05-24T18:55:47.293840image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile2000
Q12004
median2008
Q32012
95-th percentile2015
Maximum2015
Range15
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.6138409
Coefficient of variation (CV)0.0022982804
Kurtosis-1.2137217
Mean2007.5187
Median Absolute Deviation (MAD)4
Skewness-0.0064090274
Sum5898090
Variance21.287528
MonotonicityNot monotonic
2023-05-24T18:55:47.361174image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2013 193
 
6.6%
2015 183
 
6.2%
2014 183
 
6.2%
2012 183
 
6.2%
2011 183
 
6.2%
2010 183
 
6.2%
2009 183
 
6.2%
2008 183
 
6.2%
2007 183
 
6.2%
2006 183
 
6.2%
Other values (6) 1098
37.4%
ValueCountFrequency (%)
2000 183
6.2%
2001 183
6.2%
2002 183
6.2%
2003 183
6.2%
2004 183
6.2%
2005 183
6.2%
2006 183
6.2%
2007 183
6.2%
2008 183
6.2%
2009 183
6.2%
ValueCountFrequency (%)
2015 183
6.2%
2014 183
6.2%
2013 193
6.6%
2012 183
6.2%
2011 183
6.2%
2010 183
6.2%
2009 183
6.2%
2008 183
6.2%
2007 183
6.2%
2006 183
6.2%

status
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size23.1 KiB
1
2426 
0
512 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2938
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 2426
82.6%
0 512
 
17.4%

Length

2023-05-24T18:55:47.435671image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-24T18:55:47.512849image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 2426
82.6%
0 512
 
17.4%

Most occurring characters

ValueCountFrequency (%)
1 2426
82.6%
0 512
 
17.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2938
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2426
82.6%
0 512
 
17.4%

Most occurring scripts

ValueCountFrequency (%)
Common 2938
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2426
82.6%
0 512
 
17.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2938
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2426
82.6%
0 512
 
17.4%

life_expectancy
Real number (ℝ)

Distinct362
Distinct (%)12.4%
Missing10
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean69.224932
Minimum36.3
Maximum89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-05-24T18:55:47.582702image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum36.3
5-th percentile51.4
Q163.1
median72.1
Q375.7
95-th percentile82
Maximum89
Range52.7
Interquartile range (IQR)12.6

Descriptive statistics

Standard deviation9.5238675
Coefficient of variation (CV)0.13757858
Kurtosis-0.23447739
Mean69.224932
Median Absolute Deviation (MAD)5.8
Skewness-0.63860474
Sum202690.6
Variance90.704052
MonotonicityNot monotonic
2023-05-24T18:55:47.758759image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
73 45
 
1.5%
75 33
 
1.1%
78 31
 
1.1%
73.6 28
 
1.0%
73.9 25
 
0.9%
76 25
 
0.9%
81 25
 
0.9%
74.5 24
 
0.8%
74.7 24
 
0.8%
73.5 23
 
0.8%
Other values (352) 2645
90.0%
ValueCountFrequency (%)
36.3 1
< 0.1%
39 1
< 0.1%
41 1
< 0.1%
41.5 1
< 0.1%
42.3 1
< 0.1%
43.1 1
< 0.1%
43.3 1
< 0.1%
43.5 1
< 0.1%
43.8 1
< 0.1%
44 1
< 0.1%
ValueCountFrequency (%)
89 11
0.4%
88 10
0.3%
87 9
0.3%
86 15
0.5%
85 12
0.4%
84 11
0.4%
83.7 1
 
< 0.1%
83.5 2
 
0.1%
83.4 1
 
< 0.1%
83.3 1
 
< 0.1%

adult_mortality
Real number (ℝ)

Distinct425
Distinct (%)14.5%
Missing10
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean164.79645
Minimum1
Maximum723
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-05-24T18:55:47.851477image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q174
median144
Q3228
95-th percentile398.3
Maximum723
Range722
Interquartile range (IQR)154

Descriptive statistics

Standard deviation124.29208
Coefficient of variation (CV)0.75421576
Kurtosis1.7488602
Mean164.79645
Median Absolute Deviation (MAD)76
Skewness1.1743695
Sum482524
Variance15448.521
MonotonicityNot monotonic
2023-05-24T18:55:47.939431image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 34
 
1.2%
14 30
 
1.0%
16 29
 
1.0%
11 25
 
0.9%
138 25
 
0.9%
19 23
 
0.8%
144 22
 
0.7%
15 21
 
0.7%
17 21
 
0.7%
13 21
 
0.7%
Other values (415) 2677
91.1%
ValueCountFrequency (%)
1 12
0.4%
2 8
 
0.3%
3 6
 
0.2%
4 4
 
0.1%
5 2
 
0.1%
6 13
0.4%
7 16
0.5%
8 13
0.4%
9 12
0.4%
11 25
0.9%
ValueCountFrequency (%)
723 1
< 0.1%
717 1
< 0.1%
715 1
< 0.1%
699 1
< 0.1%
693 1
< 0.1%
686 1
< 0.1%
682 1
< 0.1%
679 1
< 0.1%
675 1
< 0.1%
666 1
< 0.1%

infant_deaths
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct209
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.303948
Minimum0
Maximum1800
Zeros848
Zeros (%)28.9%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-05-24T18:55:48.034376image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q322
95-th percentile94.15
Maximum1800
Range1800
Interquartile range (IQR)22

Descriptive statistics

Standard deviation117.9265
Coefficient of variation (CV)3.8914567
Kurtosis116.04276
Mean30.303948
Median Absolute Deviation (MAD)3
Skewness9.786963
Sum89033
Variance13906.66
MonotonicityNot monotonic
2023-05-24T18:55:48.122445image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 848
28.9%
1 342
 
11.6%
2 203
 
6.9%
3 175
 
6.0%
4 96
 
3.3%
8 57
 
1.9%
7 53
 
1.8%
9 48
 
1.6%
10 48
 
1.6%
6 46
 
1.6%
Other values (199) 1022
34.8%
ValueCountFrequency (%)
0 848
28.9%
1 342
11.6%
2 203
 
6.9%
3 175
 
6.0%
4 96
 
3.3%
5 44
 
1.5%
6 46
 
1.6%
7 53
 
1.8%
8 57
 
1.9%
9 48
 
1.6%
ValueCountFrequency (%)
1800 2
0.1%
1700 2
0.1%
1600 1
< 0.1%
1500 2
0.1%
1400 1
< 0.1%
1300 2
0.1%
1200 1
< 0.1%
1100 2
0.1%
1000 1
< 0.1%
957 1
< 0.1%

alcohol
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1076
Distinct (%)39.2%
Missing194
Missing (%)6.6%
Infinite0
Infinite (%)0.0%
Mean4.6028608
Minimum0.01
Maximum17.87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-05-24T18:55:48.217002image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.01
Q10.8775
median3.755
Q37.7025
95-th percentile11.96
Maximum17.87
Range17.86
Interquartile range (IQR)6.825

Descriptive statistics

Standard deviation4.0524127
Coefficient of variation (CV)0.88041174
Kurtosis-0.80290922
Mean4.6028608
Median Absolute Deviation (MAD)3.245
Skewness0.58956253
Sum12630.25
Variance16.422048
MonotonicityNot monotonic
2023-05-24T18:55:48.306152image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01 288
 
9.8%
0.03 15
 
0.5%
0.04 13
 
0.4%
0.02 12
 
0.4%
0.09 12
 
0.4%
0.21 10
 
0.3%
0.06 10
 
0.3%
1.18 10
 
0.3%
0.05 9
 
0.3%
0.49 9
 
0.3%
Other values (1066) 2356
80.2%
(Missing) 194
 
6.6%
ValueCountFrequency (%)
0.01 288
9.8%
0.02 12
 
0.4%
0.03 15
 
0.5%
0.04 13
 
0.4%
0.05 9
 
0.3%
0.06 10
 
0.3%
0.07 4
 
0.1%
0.08 9
 
0.3%
0.09 12
 
0.4%
0.1 7
 
0.2%
ValueCountFrequency (%)
17.87 1
< 0.1%
17.31 1
< 0.1%
16.99 1
< 0.1%
16.58 1
< 0.1%
16.35 1
< 0.1%
15.52 1
< 0.1%
15.19 1
< 0.1%
15.14 1
< 0.1%
15.07 1
< 0.1%
15.04 2
0.1%

percentage_expenditure
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2328
Distinct (%)79.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean738.2513
Minimum0
Maximum19479.912
Zeros611
Zeros (%)20.8%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-05-24T18:55:48.405920image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.6853426
median64.912906
Q3441.53414
95-th percentile4506.6385
Maximum19479.912
Range19479.912
Interquartile range (IQR)436.8488

Descriptive statistics

Standard deviation1987.9149
Coefficient of variation (CV)2.6927347
Kurtosis26.573387
Mean738.2513
Median Absolute Deviation (MAD)64.912906
Skewness4.6520513
Sum2168982.3
Variance3951805.5
MonotonicityNot monotonic
2023-05-24T18:55:48.497996image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 611
 
20.8%
71.27962362 1
 
< 0.1%
3.304039899 1
 
< 0.1%
218.5716179 1
 
< 0.1%
36.81621175 1
 
< 0.1%
2.542436908 1
 
< 0.1%
2.092343893 1
 
< 0.1%
22.35595448 1
 
< 0.1%
15.25518816 1
 
< 0.1%
31.50243237 1
 
< 0.1%
Other values (2318) 2318
78.9%
ValueCountFrequency (%)
0 611
20.8%
0.09987219 1
 
< 0.1%
0.108055973 1
 
< 0.1%
0.27564826 1
 
< 0.1%
0.328418056 1
 
< 0.1%
0.358651421 1
 
< 0.1%
0.388253772 1
 
< 0.1%
0.397228764 1
 
< 0.1%
0.442802404 1
 
< 0.1%
0.5305728 1
 
< 0.1%
ValueCountFrequency (%)
19479.91161 1
< 0.1%
19099.04506 1
< 0.1%
18961.3486 1
< 0.1%
18822.86732 1
< 0.1%
18379.32974 1
< 0.1%
17028.52798 1
< 0.1%
16255.16198 1
< 0.1%
15515.75234 1
< 0.1%
15345.4907 1
< 0.1%
15268.06445 1
< 0.1%

hepatitis_b
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct87
Distinct (%)3.6%
Missing553
Missing (%)18.8%
Infinite0
Infinite (%)0.0%
Mean80.940461
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-05-24T18:55:48.597285image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q177
median92
Q397
95-th percentile99
Maximum99
Range98
Interquartile range (IQR)20

Descriptive statistics

Standard deviation25.070016
Coefficient of variation (CV)0.30973403
Kurtosis2.7702594
Mean80.940461
Median Absolute Deviation (MAD)6
Skewness-1.9308451
Sum193043
Variance628.50568
MonotonicityNot monotonic
2023-05-24T18:55:48.688271image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 240
 
8.2%
98 210
 
7.1%
96 167
 
5.7%
97 155
 
5.3%
95 149
 
5.1%
94 127
 
4.3%
93 101
 
3.4%
92 92
 
3.1%
91 75
 
2.6%
89 71
 
2.4%
Other values (77) 998
34.0%
(Missing) 553
18.8%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 4
 
0.1%
4 4
 
0.1%
5 9
 
0.3%
6 17
 
0.6%
7 20
 
0.7%
8 39
1.3%
9 65
2.2%
11 1
 
< 0.1%
12 1
 
< 0.1%
ValueCountFrequency (%)
99 240
8.2%
98 210
7.1%
97 155
5.3%
96 167
5.7%
95 149
5.1%
94 127
4.3%
93 101
3.4%
92 92
 
3.1%
91 75
 
2.6%
89 71
 
2.4%

measles
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct958
Distinct (%)32.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2419.5922
Minimum0
Maximum212183
Zeros983
Zeros (%)33.5%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-05-24T18:55:48.779698image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median17
Q3360.25
95-th percentile9985.55
Maximum212183
Range212183
Interquartile range (IQR)360.25

Descriptive statistics

Standard deviation11467.272
Coefficient of variation (CV)4.7393409
Kurtosis114.8599
Mean2419.5922
Median Absolute Deviation (MAD)17
Skewness9.4413319
Sum7108762
Variance1.3149834 × 108
MonotonicityNot monotonic
2023-05-24T18:55:48.867585image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 983
33.5%
1 104
 
3.5%
2 68
 
2.3%
3 44
 
1.5%
4 33
 
1.1%
6 29
 
1.0%
7 28
 
1.0%
5 25
 
0.9%
8 24
 
0.8%
9 22
 
0.7%
Other values (948) 1578
53.7%
ValueCountFrequency (%)
0 983
33.5%
1 104
 
3.5%
2 68
 
2.3%
3 44
 
1.5%
4 33
 
1.1%
5 25
 
0.9%
6 29
 
1.0%
7 28
 
1.0%
8 24
 
0.8%
9 22
 
0.7%
ValueCountFrequency (%)
212183 1
< 0.1%
182485 1
< 0.1%
168107 1
< 0.1%
141258 1
< 0.1%
133802 1
< 0.1%
131441 1
< 0.1%
124219 1
< 0.1%
118712 1
< 0.1%
110927 1
< 0.1%
109023 1
< 0.1%

bmi
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct608
Distinct (%)20.9%
Missing34
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean38.321247
Minimum1
Maximum87.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-05-24T18:55:48.958661image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.2
Q119.3
median43.5
Q356.2
95-th percentile64.785
Maximum87.3
Range86.3
Interquartile range (IQR)36.9

Descriptive statistics

Standard deviation20.044034
Coefficient of variation (CV)0.52305275
Kurtosis-1.2910955
Mean38.321247
Median Absolute Deviation (MAD)16.3
Skewness-0.2193116
Sum111284.9
Variance401.76328
MonotonicityNot monotonic
2023-05-24T18:55:49.049100image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
58.5 18
 
0.6%
55.8 16
 
0.5%
57 16
 
0.5%
54.2 15
 
0.5%
59.9 15
 
0.5%
59.3 14
 
0.5%
52.8 13
 
0.4%
55 13
 
0.4%
59.4 13
 
0.4%
56.5 13
 
0.4%
Other values (598) 2758
93.9%
(Missing) 34
 
1.2%
ValueCountFrequency (%)
1 1
 
< 0.1%
1.4 2
 
0.1%
1.8 1
 
< 0.1%
1.9 1
 
< 0.1%
2 1
 
< 0.1%
2.1 11
0.4%
2.2 9
0.3%
2.3 6
0.2%
2.4 5
0.2%
2.5 8
0.3%
ValueCountFrequency (%)
87.3 1
< 0.1%
83.3 1
< 0.1%
82.8 1
< 0.1%
81.6 1
< 0.1%
79.3 1
< 0.1%
77.6 1
< 0.1%
77.3 1
< 0.1%
77.1 1
< 0.1%
76.7 1
< 0.1%
76.2 1
< 0.1%

under_five_deaths
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct252
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.035739
Minimum0
Maximum2500
Zeros785
Zeros (%)26.7%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-05-24T18:55:49.138446image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q328
95-th percentile138
Maximum2500
Range2500
Interquartile range (IQR)28

Descriptive statistics

Standard deviation160.44555
Coefficient of variation (CV)3.8168842
Kurtosis109.7528
Mean42.035739
Median Absolute Deviation (MAD)4
Skewness9.4950647
Sum123501
Variance25742.774
MonotonicityNot monotonic
2023-05-24T18:55:49.231102image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 785
26.7%
1 361
 
12.3%
2 163
 
5.5%
4 161
 
5.5%
3 129
 
4.4%
12 53
 
1.8%
8 49
 
1.7%
6 48
 
1.6%
10 47
 
1.6%
5 44
 
1.5%
Other values (242) 1098
37.4%
ValueCountFrequency (%)
0 785
26.7%
1 361
12.3%
2 163
 
5.5%
3 129
 
4.4%
4 161
 
5.5%
5 44
 
1.5%
6 48
 
1.6%
7 30
 
1.0%
8 49
 
1.7%
9 40
 
1.4%
ValueCountFrequency (%)
2500 1
< 0.1%
2400 1
< 0.1%
2300 1
< 0.1%
2200 1
< 0.1%
2100 1
< 0.1%
2000 2
0.1%
1900 1
< 0.1%
1800 1
< 0.1%
1700 1
< 0.1%
1600 1
< 0.1%

polio
Real number (ℝ)

Distinct73
Distinct (%)2.5%
Missing19
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean82.550188
Minimum3
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-05-24T18:55:49.323338image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile9
Q178
median93
Q397
95-th percentile99
Maximum99
Range96
Interquartile range (IQR)19

Descriptive statistics

Standard deviation23.428046
Coefficient of variation (CV)0.28380366
Kurtosis3.7765098
Mean82.550188
Median Absolute Deviation (MAD)6
Skewness-2.0980532
Sum240964
Variance548.87334
MonotonicityNot monotonic
2023-05-24T18:55:49.499241image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 376
 
12.8%
98 255
 
8.7%
96 207
 
7.0%
97 205
 
7.0%
95 180
 
6.1%
94 159
 
5.4%
93 120
 
4.1%
92 96
 
3.3%
91 88
 
3.0%
9 71
 
2.4%
Other values (63) 1162
39.6%
ValueCountFrequency (%)
3 7
 
0.2%
4 11
 
0.4%
5 8
 
0.3%
6 11
 
0.4%
7 24
 
0.8%
8 40
1.4%
9 71
2.4%
17 1
 
< 0.1%
23 1
 
< 0.1%
24 2
 
0.1%
ValueCountFrequency (%)
99 376
12.8%
98 255
8.7%
97 205
7.0%
96 207
7.0%
95 180
6.1%
94 159
5.4%
93 120
 
4.1%
92 96
 
3.3%
91 88
 
3.0%
89 56
 
1.9%

total_expenditure
Real number (ℝ)

Distinct818
Distinct (%)30.2%
Missing226
Missing (%)7.7%
Infinite0
Infinite (%)0.0%
Mean5.9381895
Minimum0.37
Maximum17.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-05-24T18:55:49.591581image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.37
5-th percentile1.93
Q14.26
median5.755
Q37.4925
95-th percentile9.76
Maximum17.6
Range17.23
Interquartile range (IQR)3.2325

Descriptive statistics

Standard deviation2.4983197
Coefficient of variation (CV)0.42072077
Kurtosis1.1562705
Mean5.9381895
Median Absolute Deviation (MAD)1.59
Skewness0.61868555
Sum16104.37
Variance6.2416012
MonotonicityNot monotonic
2023-05-24T18:55:49.682703image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.6 15
 
0.5%
6.7 12
 
0.4%
5.6 11
 
0.4%
9.1 10
 
0.3%
5.64 10
 
0.3%
5.9 10
 
0.3%
5.3 10
 
0.3%
5.25 10
 
0.3%
3.4 10
 
0.3%
4.2 9
 
0.3%
Other values (808) 2605
88.7%
(Missing) 226
 
7.7%
ValueCountFrequency (%)
0.37 1
 
< 0.1%
0.65 1
 
< 0.1%
0.74 1
 
< 0.1%
0.76 1
 
< 0.1%
0.92 1
 
< 0.1%
1.1 2
0.1%
1.12 3
0.1%
1.15 2
0.1%
1.17 2
0.1%
1.18 3
0.1%
ValueCountFrequency (%)
17.6 1
< 0.1%
17.24 1
< 0.1%
17.2 2
0.1%
17.14 1
< 0.1%
17 1
< 0.1%
16.9 1
< 0.1%
16.61 1
< 0.1%
16.2 1
< 0.1%
15.6 1
< 0.1%
15.57 1
< 0.1%

diphtheria
Real number (ℝ)

Distinct81
Distinct (%)2.8%
Missing19
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean82.324084
Minimum2
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-05-24T18:55:49.782915image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile9
Q178
median93
Q397
95-th percentile99
Maximum99
Range97
Interquartile range (IQR)19

Descriptive statistics

Standard deviation23.716912
Coefficient of variation (CV)0.28809203
Kurtosis3.558143
Mean82.324084
Median Absolute Deviation (MAD)6
Skewness-2.0727529
Sum240304
Variance562.49192
MonotonicityNot monotonic
2023-05-24T18:55:49.875847image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 350
 
11.9%
98 254
 
8.6%
97 205
 
7.0%
96 201
 
6.8%
95 200
 
6.8%
94 149
 
5.1%
93 120
 
4.1%
92 100
 
3.4%
91 91
 
3.1%
89 76
 
2.6%
Other values (71) 1173
39.9%
ValueCountFrequency (%)
2 1
 
< 0.1%
3 4
 
0.1%
4 12
 
0.4%
5 10
 
0.3%
6 16
 
0.5%
7 21
 
0.7%
8 39
1.3%
9 67
2.3%
16 1
 
< 0.1%
19 1
 
< 0.1%
ValueCountFrequency (%)
99 350
11.9%
98 254
8.6%
97 205
7.0%
96 201
6.8%
95 200
6.8%
94 149
5.1%
93 120
 
4.1%
92 100
 
3.4%
91 91
 
3.1%
89 76
 
2.6%

hiv_aids
Real number (ℝ)

Distinct200
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7421035
Minimum0.1
Maximum50.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-05-24T18:55:49.966436image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.1
Q10.1
median0.1
Q30.8
95-th percentile8.515
Maximum50.6
Range50.5
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation5.0777845
Coefficient of variation (CV)2.9147434
Kurtosis34.892008
Mean1.7421035
Median Absolute Deviation (MAD)0
Skewness5.396112
Sum5118.3
Variance25.783896
MonotonicityNot monotonic
2023-05-24T18:55:50.057645image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1 1781
60.6%
0.2 124
 
4.2%
0.3 115
 
3.9%
0.4 69
 
2.3%
0.5 42
 
1.4%
0.6 35
 
1.2%
0.9 32
 
1.1%
0.8 32
 
1.1%
0.7 29
 
1.0%
1.5 21
 
0.7%
Other values (190) 658
 
22.4%
ValueCountFrequency (%)
0.1 1781
60.6%
0.2 124
 
4.2%
0.3 115
 
3.9%
0.4 69
 
2.3%
0.5 42
 
1.4%
0.6 35
 
1.2%
0.7 29
 
1.0%
0.8 32
 
1.1%
0.9 32
 
1.1%
1 12
 
0.4%
ValueCountFrequency (%)
50.6 1
< 0.1%
50.3 1
< 0.1%
49.9 1
< 0.1%
49.1 1
< 0.1%
48.8 1
< 0.1%
46.4 1
< 0.1%
43.7 1
< 0.1%
43.5 1
< 0.1%
42.1 1
< 0.1%
40.7 1
< 0.1%

gdp
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct2490
Distinct (%)100.0%
Missing448
Missing (%)15.2%
Infinite0
Infinite (%)0.0%
Mean7483.1585
Minimum1.68135
Maximum119172.74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-05-24T18:55:50.146562image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1.68135
5-th percentile68.050015
Q1463.93563
median1766.9476
Q35910.8063
95-th percentile41606.848
Maximum119172.74
Range119171.06
Interquartile range (IQR)5446.8707

Descriptive statistics

Standard deviation14270.169
Coefficient of variation (CV)1.9069714
Kurtosis12.333074
Mean7483.1585
Median Absolute Deviation (MAD)1592.4561
Skewness3.2066549
Sum18633065
Variance2.0363773 × 108
MonotonicityNot monotonic
2023-05-24T18:55:50.242545image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
584.25921 1
 
< 0.1%
354.8185998 1
 
< 0.1%
358.99731 1
 
< 0.1%
43.646498 1
 
< 0.1%
416.14838 1
 
< 0.1%
391.515524 1
 
< 0.1%
375.5819866 1
 
< 0.1%
348.151511 1
 
< 0.1%
341.2894618 1
 
< 0.1%
292.55962 1
 
< 0.1%
Other values (2480) 2480
84.4%
(Missing) 448
 
15.2%
ValueCountFrequency (%)
1.68135 1
< 0.1%
3.685949 1
< 0.1%
4.6135745 1
< 0.1%
5.6687264 1
< 0.1%
8.376432 1
< 0.1%
11.147277 1
< 0.1%
11.33678 1
< 0.1%
11.553196 1
< 0.1%
11.631377 1
< 0.1%
12.1789279 1
< 0.1%
ValueCountFrequency (%)
119172.7418 1
< 0.1%
115761.577 1
< 0.1%
114293.8433 1
< 0.1%
113751.85 1
< 0.1%
89739.7117 1
< 0.1%
88564.82298 1
< 0.1%
87998.44468 1
< 0.1%
87646.75346 1
< 0.1%
86852.7119 1
< 0.1%
85948.746 1
< 0.1%

population
Real number (ℝ)

Distinct2278
Distinct (%)99.7%
Missing652
Missing (%)22.2%
Infinite0
Infinite (%)0.0%
Mean12753375
Minimum34
Maximum1.2938593 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-05-24T18:55:50.336256image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum34
5-th percentile9617.5
Q1195793.25
median1386542
Q37420359
95-th percentile47554416
Maximum1.2938593 × 109
Range1.2938593 × 109
Interquartile range (IQR)7224565.8

Descriptive statistics

Standard deviation61012097
Coefficient of variation (CV)4.7839961
Kurtosis298.01027
Mean12753375
Median Absolute Deviation (MAD)1357309.5
Skewness15.916236
Sum2.9154216 × 1010
Variance3.7224759 × 1015
MonotonicityNot monotonic
2023-05-24T18:55:50.432131image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
444 4
 
0.1%
718239 2
 
0.1%
1141 2
 
0.1%
26868 2
 
0.1%
127445 2
 
0.1%
292 2
 
0.1%
51448196 1
 
< 0.1%
12262 1
 
< 0.1%
15228525 1
 
< 0.1%
14668338 1
 
< 0.1%
Other values (2268) 2268
77.2%
(Missing) 652
 
22.2%
ValueCountFrequency (%)
34 1
< 0.1%
36 1
< 0.1%
41 1
< 0.1%
43 1
< 0.1%
123 1
< 0.1%
135 1
< 0.1%
146 1
< 0.1%
286 1
< 0.1%
292 2
0.1%
297 1
< 0.1%
ValueCountFrequency (%)
1293859294 1
< 0.1%
1179681239 1
< 0.1%
1161977719 1
< 0.1%
1144118674 1
< 0.1%
1126135777 1
< 0.1%
258162113 1
< 0.1%
255131116 1
< 0.1%
248883232 1
< 0.1%
242524123 1
< 0.1%
236159276 1
< 0.1%

thinness__1_19_years
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct200
Distinct (%)6.9%
Missing34
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean4.8397039
Minimum0.1
Maximum27.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-05-24T18:55:50.524984image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.6
Q11.6
median3.3
Q37.2
95-th percentile13.8
Maximum27.7
Range27.6
Interquartile range (IQR)5.6

Descriptive statistics

Standard deviation4.4201949
Coefficient of variation (CV)0.9133193
Kurtosis3.9704387
Mean4.8397039
Median Absolute Deviation (MAD)2.3
Skewness1.7114711
Sum14054.5
Variance19.538123
MonotonicityNot monotonic
2023-05-24T18:55:50.610246image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 74
 
2.5%
1.9 65
 
2.2%
0.8 64
 
2.2%
0.7 63
 
2.1%
1.2 62
 
2.1%
2.1 61
 
2.1%
1.5 60
 
2.0%
2.2 58
 
2.0%
0.9 57
 
1.9%
2 57
 
1.9%
Other values (190) 2283
77.7%
ValueCountFrequency (%)
0.1 28
 
1.0%
0.2 40
1.4%
0.3 32
1.1%
0.4 5
 
0.2%
0.5 35
1.2%
0.6 41
1.4%
0.7 63
2.1%
0.8 64
2.2%
0.9 57
1.9%
1 74
2.5%
ValueCountFrequency (%)
27.7 1
 
< 0.1%
27.5 1
 
< 0.1%
27.4 1
 
< 0.1%
27.3 1
 
< 0.1%
27.2 2
0.1%
27.1 2
0.1%
27 3
0.1%
26.9 2
0.1%
26.8 2
0.1%
26.7 1
 
< 0.1%

thinness_5_9_years
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct207
Distinct (%)7.1%
Missing34
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean4.8703168
Minimum0.1
Maximum28.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-05-24T18:55:50.702658image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.5
Q11.5
median3.3
Q37.2
95-th percentile13.8
Maximum28.6
Range28.5
Interquartile range (IQR)5.7

Descriptive statistics

Standard deviation4.5088821
Coefficient of variation (CV)0.92578825
Kurtosis4.3587303
Mean4.8703168
Median Absolute Deviation (MAD)2.3
Skewness1.777424
Sum14143.4
Variance20.330018
MonotonicityNot monotonic
2023-05-24T18:55:50.792592image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9 69
 
2.3%
1.1 67
 
2.3%
0.5 63
 
2.1%
1.9 63
 
2.1%
1 62
 
2.1%
2.1 61
 
2.1%
1.3 59
 
2.0%
1.5 57
 
1.9%
1.7 55
 
1.9%
0.6 54
 
1.8%
Other values (197) 2294
78.1%
ValueCountFrequency (%)
0.1 37
1.3%
0.2 45
1.5%
0.3 25
 
0.9%
0.4 17
 
0.6%
0.5 63
2.1%
0.6 54
1.8%
0.7 46
1.6%
0.8 36
1.2%
0.9 69
2.3%
1 62
2.1%
ValueCountFrequency (%)
28.6 1
< 0.1%
28.5 1
< 0.1%
28.4 1
< 0.1%
28.3 1
< 0.1%
28.2 1
< 0.1%
28.1 1
< 0.1%
28 2
0.1%
27.9 1
< 0.1%
27.8 2
0.1%
27.7 1
< 0.1%

income_composition_of_resources
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct625
Distinct (%)22.6%
Missing167
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean0.62755106
Minimum0
Maximum0.948
Zeros130
Zeros (%)4.4%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-05-24T18:55:50.888593image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.277
Q10.493
median0.677
Q30.779
95-th percentile0.892
Maximum0.948
Range0.948
Interquartile range (IQR)0.286

Descriptive statistics

Standard deviation0.21090356
Coefficient of variation (CV)0.33607393
Kurtosis1.3928142
Mean0.62755106
Median Absolute Deviation (MAD)0.127
Skewness-1.1437627
Sum1738.944
Variance0.04448031
MonotonicityNot monotonic
2023-05-24T18:55:50.980126image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 130
 
4.4%
0.7 17
 
0.6%
0.739 13
 
0.4%
0.714 12
 
0.4%
0.636 12
 
0.4%
0.737 11
 
0.4%
0.734 11
 
0.4%
0.797 11
 
0.4%
0.86 11
 
0.4%
0.703 11
 
0.4%
Other values (615) 2532
86.2%
(Missing) 167
 
5.7%
ValueCountFrequency (%)
0 130
4.4%
0.253 1
 
< 0.1%
0.255 1
 
< 0.1%
0.261 1
 
< 0.1%
0.266 1
 
< 0.1%
0.268 3
 
0.1%
0.27 1
 
< 0.1%
0.276 1
 
< 0.1%
0.278 1
 
< 0.1%
0.279 1
 
< 0.1%
ValueCountFrequency (%)
0.948 1
 
< 0.1%
0.945 1
 
< 0.1%
0.942 1
 
< 0.1%
0.941 1
 
< 0.1%
0.939 1
 
< 0.1%
0.938 1
 
< 0.1%
0.937 1
 
< 0.1%
0.936 5
0.2%
0.934 2
 
0.1%
0.933 1
 
< 0.1%

schooling
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct173
Distinct (%)6.2%
Missing163
Missing (%)5.5%
Infinite0
Infinite (%)0.0%
Mean11.992793
Minimum0
Maximum20.7
Zeros28
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size23.1 KiB
2023-05-24T18:55:51.158509image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.8
Q110.1
median12.3
Q314.3
95-th percentile16.8
Maximum20.7
Range20.7
Interquartile range (IQR)4.2

Descriptive statistics

Standard deviation3.3589197
Coefficient of variation (CV)0.28007819
Kurtosis0.88615127
Mean11.992793
Median Absolute Deviation (MAD)2.1
Skewness-0.60243654
Sum33280
Variance11.282342
MonotonicityNot monotonic
2023-05-24T18:55:51.249400image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.9 58
 
2.0%
13.3 52
 
1.8%
12.5 49
 
1.7%
12.8 46
 
1.6%
12.3 44
 
1.5%
12.6 43
 
1.5%
12.4 42
 
1.4%
10.7 41
 
1.4%
11.9 41
 
1.4%
12.7 40
 
1.4%
Other values (163) 2319
78.9%
(Missing) 163
 
5.5%
ValueCountFrequency (%)
0 28
1.0%
2.8 1
 
< 0.1%
2.9 4
 
0.1%
3 1
 
< 0.1%
3.1 1
 
< 0.1%
3.3 1
 
< 0.1%
3.4 1
 
< 0.1%
3.5 3
 
0.1%
3.6 1
 
< 0.1%
3.7 2
 
0.1%
ValueCountFrequency (%)
20.7 1
 
< 0.1%
20.6 1
 
< 0.1%
20.5 1
 
< 0.1%
20.4 3
0.1%
20.3 4
0.1%
20.1 2
0.1%
19.8 1
 
< 0.1%
19.7 1
 
< 0.1%
19.5 3
0.1%
19.3 2
0.1%

Interactions

2023-05-24T18:55:44.676230image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:14.953392image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:16.647258image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:18.159205image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:19.881165image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:21.437050image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:23.067800image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:24.690656image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:26.256791image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:27.753129image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:29.202986image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:30.771303image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:32.234746image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:33.865067image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:35.330703image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:36.880748image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:38.396786image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:39.955840image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:41.579781image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:43.105496image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:44.752418image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:15.139359image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:16.730919image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:18.242268image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:19.964092image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:21.518609image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:23.151901image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:24.770795image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:26.334264image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:27.829986image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:29.281390image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:30.848823image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:32.318106image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:33.942997image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:35.407847image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:36.959961image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:38.477651image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:40.036011image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:41.661449image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:43.183152image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:44.823657image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:15.228116image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:16.803758image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:18.322068image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:20.039471image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:21.594072image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:23.232936image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:24.841197image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:26.412384image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:27.905121image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:29.442820image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:30.920654image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:32.393630image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:34.013936image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:35.481003image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:37.034727image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:38.553459image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:40.113126image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:41.736912image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:43.256805image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:44.899118image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:15.313791image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:16.882481image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:18.402143image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:20.120638image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:21.674851image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:23.316025image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:24.918905image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:26.489029image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:27.987791image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:29.520300image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:30.997133image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:32.475951image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:34.091390image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:35.557225image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:37.113301image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:38.633321image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:40.192134image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:41.818610image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:43.334437image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:44.974114image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:15.398881image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:16.961022image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:18.581746image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:20.198883image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:21.755720image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:23.398952image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:24.999661image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:26.576058image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:28.061887image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:29.597905image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:31.074982image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:32.557494image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:34.168795image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:35.634262image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:37.191848image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:38.717042image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:40.377540image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:41.899249image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:43.413497image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:45.049721image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:15.491262image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:17.040563image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:18.677925image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:20.280466image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:21.835541image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:23.483304image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:25.076661image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:26.673358image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:28.138820image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:29.675360image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:31.151553image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:32.637046image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:34.246820image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:35.711148image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:37.270403image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:38.800387image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:40.457300image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:41.980937image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:43.491886image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:45.165641image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:15.576013image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:17.122587image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:18.763581image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:20.366929image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:22.014279image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:23.568883image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:25.155456image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:26.755576image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:28.216996image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:29.756809image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:31.232331image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:32.722567image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:34.327470image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:35.794279image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:37.352831image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:38.883935image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:40.540987image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:42.063738image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:43.575263image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:45.235315image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:15.650922image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:17.193126image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:18.843525image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:20.442819image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:22.089402image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:23.644823image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:25.225126image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-05-24T18:55:28.994266image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:30.553430image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:32.022241image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:33.636620image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:35.116462image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:36.589679image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:38.175256image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:39.717441image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:41.357568image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:42.884526image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:44.462695image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:46.010675image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:16.500617image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:18.016207image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:19.727159image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:21.284231image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:22.923421image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:24.531191image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:26.114632image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:27.614221image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:29.067386image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:30.628901image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:32.095738image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:33.716527image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:35.191271image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:36.662969image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:38.252590image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:39.795571image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:41.434392image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:42.960353image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:44.538420image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:46.080326image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:16.574871image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:18.088440image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:19.804701image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:21.363756image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:22.997079image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:24.610073image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:26.186114image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:27.686095image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:29.136206image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:30.700655image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:32.167872image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:33.792825image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:35.261570image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:36.813644image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:38.325075image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:39.875116image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:41.509384image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:43.035557image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-24T18:55:44.607112image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-05-24T18:55:51.342867image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
yearlife_expectancyadult_mortalityinfant_deathsalcoholpercentage_expenditurehepatitis_bmeaslesbmiunder_five_deathspoliototal_expenditurediphtheriahiv_aidsgdppopulationthinness__1_19_yearsthinness_5_9_yearsincome_composition_of_resourcesschoolingstatus
year1.0000.157-0.053-0.052-0.101-0.0500.099-0.0950.149-0.0520.1090.0800.132-0.0560.1810.045-0.041-0.0390.2020.1950.000
life_expectancy0.1571.000-0.650-0.6010.4430.4290.350-0.2810.585-0.6190.5350.2940.545-0.7540.642-0.090-0.611-0.6210.8660.8140.627
adult_mortality-0.053-0.6501.0000.392-0.217-0.297-0.2270.146-0.3930.405-0.319-0.175-0.3280.523-0.3830.0970.3890.404-0.548-0.4960.366
infant_deaths-0.052-0.6010.3921.000-0.382-0.361-0.3430.573-0.4800.993-0.430-0.218-0.4260.487-0.5080.4500.4530.468-0.578-0.5980.065
alcohol-0.1010.443-0.217-0.3821.0000.3030.114-0.1980.324-0.3810.2610.3390.277-0.1970.427-0.009-0.466-0.4600.5140.5510.667
percentage_expenditure-0.0500.429-0.297-0.3610.3031.0000.102-0.1530.279-0.3620.2100.1650.224-0.2550.807-0.065-0.305-0.3060.5060.4890.448
hepatitis_b0.0990.350-0.227-0.3430.1140.1021.000-0.2230.195-0.3430.7930.0450.817-0.3370.256-0.115-0.045-0.0620.3560.3580.172
measles-0.095-0.2810.1460.573-0.198-0.153-0.2231.000-0.2770.574-0.268-0.187-0.2660.204-0.2140.2960.3110.325-0.229-0.2820.022
bmi0.1490.585-0.393-0.4800.3240.2790.195-0.2771.000-0.4910.3250.2670.335-0.5180.481-0.068-0.564-0.5740.6180.6150.459
under_five_deaths-0.052-0.6190.4050.993-0.381-0.362-0.3430.574-0.4911.000-0.434-0.223-0.4290.512-0.5140.4430.4610.474-0.589-0.6090.060
polio0.1090.535-0.319-0.4300.2610.2100.793-0.2680.325-0.4341.0000.1420.921-0.4850.395-0.098-0.220-0.2300.5270.5240.304
total_expenditure0.0800.294-0.175-0.2180.3390.1650.045-0.1870.267-0.2230.1421.0000.158-0.1430.156-0.093-0.360-0.3760.2200.2900.431
diphtheria0.1320.545-0.328-0.4260.2770.2240.817-0.2660.335-0.4290.9210.1581.000-0.4710.403-0.088-0.233-0.2420.5320.5290.313
hiv_aids-0.056-0.7540.5230.487-0.197-0.255-0.3370.204-0.5180.512-0.485-0.143-0.4711.000-0.4790.0930.4760.463-0.649-0.6180.126
gdp0.1810.642-0.383-0.5080.4270.8070.256-0.2140.481-0.5140.3950.1560.403-0.4791.000-0.049-0.419-0.4280.6950.6650.478
population0.045-0.0900.0970.450-0.009-0.065-0.1150.296-0.0680.443-0.098-0.093-0.0880.093-0.0491.0000.0770.089-0.055-0.0700.053
thinness__1_19_years-0.041-0.6110.3890.453-0.466-0.305-0.0450.311-0.5640.461-0.220-0.360-0.2330.476-0.4190.0771.0000.947-0.577-0.5760.462
thinness_5_9_years-0.039-0.6210.4040.468-0.460-0.306-0.0620.325-0.5740.474-0.230-0.376-0.2420.463-0.4280.0890.9471.000-0.576-0.5770.466
income_composition_of_resources0.2020.866-0.548-0.5780.5140.5060.356-0.2290.618-0.5890.5270.2200.532-0.6490.695-0.055-0.577-0.5761.0000.9010.706
schooling0.1950.814-0.496-0.5980.5510.4890.358-0.2820.615-0.6090.5240.2900.529-0.6180.665-0.070-0.576-0.5770.9011.0000.643
status0.0000.6270.3660.0650.6670.4480.1720.0220.4590.0600.3040.4310.3130.1260.4780.0530.4620.4660.7060.6431.000

Missing values

2023-05-24T18:55:46.201648image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-24T18:55:46.435175image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-05-24T18:55:46.635490image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

countryyearstatuslife_expectancyadult_mortalityinfant_deathsalcoholpercentage_expenditurehepatitis_bmeaslesbmiunder_five_deathspoliototal_expenditurediphtheriahiv_aidsgdppopulationthinness__1_19_yearsthinness_5_9_yearsincome_composition_of_resourcesschooling
0Afghanistan2015165.0263.0620.0171.27962465.0115419.1836.08.1665.00.1584.25921033736494.017.217.30.47910.1
1Afghanistan2014159.9271.0640.0173.52358262.049218.68658.08.1862.00.1612.696514327582.017.517.50.47610.0
2Afghanistan2013159.9268.0660.0173.21924364.043018.18962.08.1364.00.1631.74497631731688.017.717.70.4709.9
3Afghanistan2012159.5272.0690.0178.18421567.0278717.69367.08.5267.00.1669.9590003696958.017.918.00.4639.8
4Afghanistan2011159.2275.0710.017.09710968.0301317.29768.07.8768.00.163.5372312978599.018.218.20.4549.5
5Afghanistan2010158.8279.0740.0179.67936766.0198916.710266.09.2066.00.1553.3289402883167.018.418.40.4489.2
6Afghanistan2009158.6281.0770.0156.76221763.0286116.210663.09.4263.00.1445.893298284331.018.618.70.4348.9
7Afghanistan2008158.1287.0800.0325.87392564.0159915.711064.08.3364.00.1373.3611162729431.018.818.90.4338.7
8Afghanistan2007157.5295.0820.0210.91015663.0114115.211363.06.7363.00.1369.83579626616792.019.019.10.4158.4
9Afghanistan2006157.3295.0840.0317.17151864.0199014.711658.07.4358.00.1272.5637702589345.019.219.30.4058.1
countryyearstatuslife_expectancyadult_mortalityinfant_deathsalcoholpercentage_expenditurehepatitis_bmeaslesbmiunder_five_deathspoliototal_expenditurediphtheriahiv_aidsgdppopulationthinness__1_19_yearsthinness_5_9_yearsincome_composition_of_resourcesschooling
2928Zimbabwe2009150.0587.0304.641.04002173.085329.04569.06.2673.018.165.8241211381599.07.57.40.4199.9
2929Zimbabwe2008148.2632.0303.5620.84342975.0028.64675.04.9675.020.5325.67857313558469.07.87.80.4219.7
2930Zimbabwe2007146.667.0293.8829.81456672.024228.24673.04.4773.023.7396.9982171332999.08.28.20.4149.6
2931Zimbabwe2006145.47.0284.5734.26216968.021227.94571.05.127.026.8414.79623213124267.08.68.60.4089.5
2932Zimbabwe2005144.6717.0284.148.71740965.042027.54369.06.4468.030.3444.765750129432.09.09.00.4069.3
2933Zimbabwe2004144.3723.0274.360.00000068.03127.14267.07.1365.033.6454.36665412777511.09.49.40.4079.2
2934Zimbabwe2003144.5715.0264.060.0000007.099826.7417.06.5268.036.7453.35115512633897.09.89.90.4189.5
2935Zimbabwe2002144.873.0254.430.00000073.030426.34073.06.5371.039.857.348340125525.01.21.30.42710.0
2936Zimbabwe2001145.3686.0251.720.00000076.052925.93976.06.1675.042.1548.58731212366165.01.61.70.4279.8
2937Zimbabwe2000146.0665.0241.680.00000079.0148325.53978.07.1078.043.5547.35887812222251.011.011.20.4349.8